Decision Support and Image & Signal Analysis in Heart Failure A Comprehensive Use Case

1. Decision Support and Image & Signal Analysis in Heart FailureA Comprehensive Use Case

2. Foreword This research work is being carried out within the European STREP project HEARTFAID • Thematic Priority: Information Society Technology – ICT for Health • Instrument: STREP • Project Identifier: IST-2005-027107 • Time-table: 2006-2009 • Project web site:www.heartfaid.org Consortium Knowledge Discovery and Decision Support Systems in Health Information Systems

3. Outline HEARTFAID Project HEARTFAID Decision Support and Data Processing Services A Significant Scenario Current Results Conclusions & Future Activities Knowledge Discovery and Decision Support Systems in Health Information Systems

4. HEARTFAID Project HEARTFAID Project • A KNOWLEDGE BASED PLATFORM OF SERVICES • for supporting medical-clinical management of HEART FAILURE (HF) within elderly population • By devising: • an innovative technological platform for informative and decision support based on an umbrella of services • defining new health care delivery organization integrating different health care environments and operators for a patient centric management program Knowledge Discovery and Decision Support Systems in Health Information Systems

5. HEARTFAID Project HEARTFAID Platform of Services • Data Collection & Management • integration of heterogeneous data from biomedical devices, clinical reports, telemonitoring • Knowledge-based Decision Support • supporting the HF health care operators in health care personalization of the HF patients • End-user Applications • doorway to a multitude of end-user utilities and services, such as accessing an electronic health record, querying the clinical decision support system Data Collection and Management End-user Applications Knowledge-based Decision Support Knowledge Discovery and Decision Support Systems in Health Information Systems

6. HEARTFAID Project Core HEARTFAID Platform Components Thecoreof HEARTFAIDintelligence • A Knowledge base for the relevant medical domain • Innovative inference engines methodologies for medical decision support • Innovative approaches for biomedical signal and image processing Brain Heart HEARTFAIDClinical Decision Support System (CDSS) Knowledge Discovery and Decision Support Systems in Health Information Systems

7. HEARTFAID Decision Support and Data Processing Services HEARTFAID Decision Support and Data Processing Services Accurate design activities • Signals and Images Processing: • Diagnostic resources investigation • Data processing relevance in routine and research workflows • Decision Support: • Methodological foundations and technological State of the Art analysis • Heart Failure Domain investigation Knowledge Discovery and Decision Support Systems in Health Information Systems

8. HEARTFAID Decision Support and Data Processing Services Data Processing: Resource Investigation 1D Signals Holter ECG Exercise • Diagnostic Resource Significance • Representation Features Echo Chest X-ray 2D Images MRI Nuclear 3D Images Knowledge Discovery and Decision Support Systems in Health Information Systems

9. HEARTFAID Decision Support and Data Processing Services Data Processing: Routine vs. Innovation Routine Clinical Practice – HFP Level 2 • Fill instrumentation lacks • Reduce analysis subjectivity Reduceintra/inter-observer variability Open-problems Assessment Long -Term Research Environment – HFP Level 4 Correlation Analysis Signal/Image Categorization • Extend HF knowledge Extractinnovative representing features Knowledge Discovery and Decision Support Systems in Health Information Systems

10. HEARTFAID Decision Support and Data Processing Services Decision Support: Methodological Foundations Analysis DSS KB-DSS CDSS HEARTFAID CDSS Knowledge Discovery and Decision Support Systems in Health Information Systems

11. HEARTFAID Decision Support and Data Processing Services Heart Failure Domain Investigation • Analysis of the medical domain for individuating the decision problemsthat require HEARTFAID CDSS intervention Specific Problems • Diagnosis: • Assessment and severity evaluation of heart failure • Analysis of diagnostic exams • Prognosis • Prognosis stratification • Therapy: • Identification of suitable pathways • Planning of adequate, patient’s specific therapy • Follow-up: • Suggestion of changes in management and treatment • Early detection of patient’s decompensation HEARTFAID Problem Domains • Diagnosis • Prognosis • Therapy • Follow-up Knowledge Discovery and Decision Support Systems in Health Information Systems

12. CDSS Brain HEARTFAID Decision Support and Data Processing Services Design Choices For some complex problem, e.g. early decompensation, no knowledge available: novel knowledge extracted by means of Artificial Neural Networks; Support Vector Machines or Bayesian Networks,… Rule-based knowledge representation formalism more similar to experts’ reasoning mechanisms and simpler to understand for them Algorithms for supporting clinicians’ interpretations of diagnostic exams + Ontologies helps standardizing terminology Computational Reasoning Data Processing Algorithms Inferential Reasoning Knowledge Discovery and Decision Support Systems in Health Information Systems

13. HEARTFAID Decision Support and Data Processing Services HEARTFAID CDSS Conceptual Modeling • Knowledge vs. Processing levels System components that are responsible for tasks accomplishment by using the knowledge level All the information needed by the system for performing tasks (e.g. data, domain knowledge, computational decision models) Processing Level Knowledge Level Knowledge Discovery and Decision Support Systems in Health Information Systems

14. HEARTFAID Decision Support and Data Processing Services HEARTFAID CDSS Architecture Formalized Experts’ Knowledge Computational Reasoning models + Data Processing Algorithms Knowledge Discovery and Decision Support Systems in Health Information Systems

15. A Significant Scenario A Significant Scenario • Sixty-five years old patient enrolled in HFP • History of acute myocardial infarction, aorto-coronary bypass • Currently, ischaemic dilated cardiomyopathy, with systolic disfunction • HEARTFAID • detects worsening of symptoms by telemonitoring; • schedules a new visit; • suggests clinical examinations; • interprets findings; • suggests new therapy; • gives hints about possible origins of symptoms worsening Knowledge Discovery and Decision Support Systems in Health Information Systems

16. A Significant Scenario Mapping onto the HEARTFAID CDSS • The scenario has used for defining the functioning of the CDSS • Each component of CDSS is triggered • Example of mapping for the worsening of symptoms detection by telemonitoring Knowledge Discovery and Decision Support Systems in Health Information Systems

17. Patients Repository End User Applications Minnesota Questionnaire CDSS Agenda //schedule a visit A Significant Scenario Telemonitoring: Worsening HEARTFAID Platform Inference on patient’s data Ischemic Cardiomyopathy + marked activity limitation Alert: A1, Priority: P5 Telemonitoring CDSS Functionality: Telemonitoring Data Acquisition Visit Scheduled Request: Interpret new data Strategy: Trigger Inference Engine call(InfEng(telemon, P,data_KB); … • Alert: A1 • Priority: P5 Knowledge Discovery and Decision Support Systems in Health Information Systems

18. W3C Stack of Instruments Requirements Current Results Results • Development tools were selected for fulfilling requirements of • Open Source • Upgradeability • Robustness • Ease of use • Semantic Web Technologieshas gathered attention within Decision Support Theory since offer • data integration • knowledge representation • reasoning • We followed the recommendations of World Wide Web Consortium (W3C) Knowledge Discovery and Decision Support Systems in Health Information Systems

19. Current Results Development Languages and Tools • Web Ontology Language (OWL) for defining ontologies • Protégéas ontology editor • Semantic Web Rule Language combining OWL and Rule Mark-up Language (SWRL) • Jena was selected as a Java programmatic environment that includes OWL, a language for querying ontologies, SPARQL, and a rule-based inference engines • Other two reasoners: Bossam, Pellet Knowledge Discovery and Decision Support Systems in Health Information Systems

20. Current Results Knowledge Representation • A coherent and comprehensive formalization of HF domain was elicited from the guidelines of the European Society of Cardiology and a strong interaction with clinicians, • Starting from an existing ontology, new classes and relations were added, also in accordance to standard medical ontologies (e.g., Unified Medical Language System, UMLS) • An excerpt of the ontology: Knowledge Discovery and Decision Support Systems in Health Information Systems

21. Current Results Rules Formalization • A set of rules was defined for the scenario • Example of natural language elicitation “If a patient has Left Ventricle Ejection Fraction <= 40%and he is asymptomatic and is assuming ACE Inhibitors and he had a myocardial infarction then a suggestion for the doctor is to give the patient Betablockers” • Translation into SWRL language Knowledge Discovery and Decision Support Systems in Health Information Systems

22. 4C 2C ED ES Current Results Image Processing • Automated computation of Left Ventricle (LV) Ejection Fraction from Echocardiography images Simpson’s method: EDV=end diastolic volume ESV=end systolic volume EF=ejection fraction • Delineation of LV cavity • Computation of volumes and axis • Refinements: • level set method for accurate LV contour identification Knowledge Discovery and Decision Support Systems in Health Information Systems

23. Current Results Image Processing • Extraction of LV contours: Refinement step Knowledge Discovery and Decision Support Systems in Health Information Systems

24. Current Results Signal Processing • Focus on ECG • QRS detection • QRS classification • Dominant beat averaging (SNR enhancement in order to provide the cardiologist with a clearer beat on which to operate the measurements) Knowledge Discovery and Decision Support Systems in Health Information Systems

25. Current Results The Annotated Database • Real data (surface ECGs) are used from the MIT-BIH Arrhythmia Database for a total of 48 records half-hour excerpts of two-channel ambulatory ECG recordings • The recordings are digitized at 360 Hz with 11-bit resolution over a 10 mV range • Two or more cardiologists independently annotated each record; disagreements were resolved to obtain the computer-readable reference annotations for each beat (approximately 110,000 annotations in all) included in the database Knowledge Discovery and Decision Support Systems in Health Information Systems

26. Current Results QRS Detection • Pre-filtering using a band-pass filter in the band 5-15 Hz • The band-pass filtered signals are used for the creation of a QRS enhanced signal (QeS) • The QeS is built as the sum of the absolute derivatives of each channel • An adaptive threshold is used for the QRS detection. The threshold is continuously updated after each QRS detection • To avoid indicating a T-wave (and especially large-amplitude T-waves) as another QRS, the QRS detection threshold is artificially increased after detecting a QRS peak • A dead-time zone of 200 msec is set up in order to reject any QRS detection closer than 200 msec to the previous one Knowledge Discovery and Decision Support Systems in Health Information Systems

27. Current Results Results of the QRS Detection • The total number of annotated beats results 109494, with 109288 TP; FN and FP are respectively 266 and 210 • The sensitivity TP/(TP+FN) is 99.76% while the positive predictive value (PPV) TP/(TP+FP) is 99.81% • In 15 records a perfect detection without any FN and FP has been obtained • 12 records have more than 10 FP+FN and only 5 records more than 30 FP+FN • Sensitivity and PPV are equal or better of other algorithm published in literature and the results are obtained on all beats of the entire database while in some published studies only a subset of the total beats has been used • On a data set of 75 ECGs provided by Univ. Magna Graecia we had only 1 FN and 0 FP. Knowledge Discovery and Decision Support Systems in Health Information Systems

28. Current Results QRS Classification • Feature extraction from the detected beats • Two-step decision tree classification • Preliminary results on the MIT-BIH arrhythmia database: • Specificity 93.70% • Sensitivity 98.71% • PPV 99.29% • NPV 89.02% Knowledge Discovery and Decision Support Systems in Health Information Systems

29. IV Current Results User Interface for Patient’s Management Knowledge Discovery and Decision Support Systems in Health Information Systems

30. Conclusions & Future Activities Conclusions & Future Activities • Shown the current results of HEARTFAID Clinical Decision Support and Signals&Images Processing • Innovative conceptual modeling • Novel methods for aiding diagnostic examination • Clinicians are satisfied of preliminary results • Activities will be finalized in 2009 by concluding • The Domain Knowledge Base • Algorithms contained in the Model Base • The Signals and images analysis toolkits • The Meta level for integrating all the object models and the interface • The integration with the other platform components Knowledge Discovery and Decision Support Systems in Health Information Systems

31. Decision Support and Image & Signal Analysis in Heart FailureA Comprehensive Use Case Thanks